Introduction
I this notebook we ingest and visualize the mobility trends data provided by Apple, [APPL1].
We take the following steps:
Download the data
Import the data and summarise it
Transform the data into long form
Partition the data into subsets that correspond to combinations of geographical regions and transportation types
Make contingency matrices and corresponding heat-map plots
Make nearest neighbors graphs over the contingency matrices and plot communities
Plot the corresponding time series
Data description
About This Data The CSV file and charts on this site show a relative volume of directions requests per country/region or city compared to a baseline volume on January 13th, 2020. We define our day as midnight-to-midnight, Pacific time. Cities represent usage in greater metropolitan areas and are stably defined during this period. In many countries/regions and cities, relative volume has increased since January 13th, consistent with normal, seasonal usage of Apple Maps. Day of week effects are important to normalize as you use this data. Data that is sent from users’ devices to the Maps service is associated with random, rotating identifiers so Apple doesn’t have a profile of your movements and searches. Apple Maps has no demographic information about our users, so we can’t make any statements about the representativeness of our usage against the overall population.
Observations
The observations listed in this subsection are also placed under the relevant statistics in the following sections and indicated with “Observation”.
The directions requests volumes reference date for normalization is 2020-01-13 : all the values in that column are \(100\).
From the community clusters of the nearest neighbor graphs (derived from the time series of the normalized driving directions requests volume) we see that countries and cities are clustered in expected ways. For example, in the community graph plot corresponding to “{city, driving}” the cities Oslo, Copenhagen, Helsinki, Stockholm, and Zurich are placed in the same cluster. In the graphs corresponding to “{city, transit}” and “{city, walking}” the Japanese cities Tokyo, Osaka, Nagoya, and Fukuoka are clustered together.
In the time series plots the Sundays are indicated with orange dashed lines. We can see that from Monday to Thursday people are more familiar with their trips than say on Fridays and Saturdays. We can also see that on Sundays people (on average) are more familiar with their trips or simply travel less.
Load packages
library(Matrix)
library(tidyverse)
library(ggplot2)
library(gridExtra)
library(d3heatmap)
library(igraph)
library(zoo)
Data ingestion
Apple mobile data was provided in this WWW page: https://www.apple.com/covid19/mobility , [APPL1]. (The data has to be download from that web page – there is an “agreement to terms”, etc.)
dfAppleMobility <- read.csv( "~/Downloads/applemobilitytrends-2020-05-26.csv", stringsAsFactors = FALSE)
names(dfAppleMobility) <- gsub( "^X", "", names(dfAppleMobility))
names(dfAppleMobility) <- gsub( ".", "-", names(dfAppleMobility), fixed = TRUE)
dfAppleMobility
Observation: The directions requests volumes reference date for normalization is 2020-01-13 : all the values in that column are \(100\).
Data dimensions:
dim(dfAppleMobility)
[1] 3625 141
Data summary:
summary(as.data.frame(unclass(dfAppleMobility[,1:3]), stringsAsFactors = TRUE))
geo_type region transportation_type
city : 786 Washington County: 22 driving:3048
country/region: 153 Franklin County : 20 transit: 222
county :2090 Jefferson County : 19 walking: 355
sub-region : 596 Madison County : 18
Jackson County : 16
Montgomery County: 15
(Other) :3515
Number of unique “country/region” values:
dfAppleMobility %>%
dplyr::filter( geo_type == "country/region") %>%
dplyr::pull("region") %>%
unique %>%
length
[1] 63
Number of unique “city” values:
dfAppleMobility %>%
dplyr::filter( geo_type == "city") %>%
dplyr::pull("region") %>%
unique %>%
length
[1] 295
All unique geo types:
lsGeoTypes <- unique(dfAppleMobility[["geo_type"]])
lsGeoTypes
[1] "country/region" "city" "sub-region" "county"
All unique transportation types:
lsTransportationTypes <- unique(dfAppleMobility[["transportation_type"]])
lsTransportationTypes
[1] "driving" "walking" "transit"
Data transformation
It is better to have the data in long form (narrow form). For that I am using the package “tidyr”.
# lsIDColumnNames <- c("geo_type", "region", "transportation_type") # For the initial dataset released by Apple.
lsIDColumnNames <- c("geo_type", "region", "transportation_type", "alternative_name", "sub-region", "country" )
dfAppleMobilityLongForm <- tidyr::pivot_longer( data = dfAppleMobility, cols = setdiff( names(dfAppleMobility), lsIDColumnNames), names_to = "Date", values_to = "Value" )
dim(dfAppleMobilityLongForm)
[1] 489375 8
Remove the rows with “empty” values:
dfAppleMobilityLongForm <- dfAppleMobilityLongForm[ complete.cases(dfAppleMobilityLongForm), ]
dim(dfAppleMobilityLongForm)
[1] 476938 8
Add the “DateObject” column:
dfAppleMobilityLongForm$DateObject <- as.POSIXct( dfAppleMobilityLongForm$Date, format = "%Y-%m-%d", origin = "1970-01-01" )
Add “day name” (“day of the week”) field:
dfAppleMobilityLongForm$DayName <- weekdays(dfAppleMobilityLongForm$DateObject)
Here is sample of the transformed data:
set.seed(3232)
dfAppleMobilityLongForm %>% dplyr::sample_n( 10 )
Here is summary:
summary(as.data.frame(unclass(dfAppleMobilityLongForm), stringsAsFactors = TRUE))
geo_type region transportation_type alternative_name sub.region country Date Value
city :104538 Washington County: 2926 driving:400197 :382907 :107065 United States:327180 2020-01-13: 3586 Min. : 2.43
country/region: 20349 Franklin County : 2660 transit: 29526 's-Gravenhage|Den Haag: 399 Texas : 26999 : 20349 2020-01-14: 3586 1st Qu.: 73.04
county :277970 Jefferson County : 2527 walking: 47215 Anvers|Antwerpen : 399 California: 14896 Japan : 16891 2020-01-15: 3586 Median : 99.43
sub-region : 74081 Madison County : 2394 AU : 399 Georgia : 14630 Germany : 8379 2020-01-16: 3586 Mean : 96.65
Jackson County : 2128 Bâle : 399 Virginia : 13566 Thailand : 8246 2020-01-17: 3586 3rd Qu.: 116.79
Montgomery County: 1995 België|Belgique : 399 Ohio : 13433 France : 7448 2020-01-18: 3586 Max. :1081.37
(Other) :462308 (Other) : 92036 (Other) :286349 (Other) : 88445 (Other) :455422
DateObject DayName
Min. :2020-01-13 00:00:00 Friday :68134
1st Qu.:2020-02-15 00:00:00 Monday :68134
Median :2020-03-19 00:00:00 Saturday :68134
Mean :2020-03-19 05:28:25 Sunday :68134
3rd Qu.:2020-04-21 00:00:00 Thursday :68134
Max. :2020-05-26 00:00:00 Tuesday :68134
Wednesday:68134
Partition the data into geo types × transportation types:
dfAppleMobilityLongForm %>%
dplyr::group_by( geo_type, transportation_type) %>%
dplyr::count()
aQueries <- split(dfAppleMobilityLongForm, dfAppleMobilityLongForm[,c("geo_type", "transportation_type")] )
Heat-map plots
We can visualize the data using heat-map plots.
Remark: Using the contingency matrices prepared for the heat-map plots we can do further analysis, like, finding correlations or nearest neighbors. (See below.)
Cross-tabulate dates with regions:
aMatDateRegion <- purrr::map( aQueries, function(dfX) { xtabs( formula = Value ~ Date + region, data = dfX, sparse = TRUE ) } )
aMatDateRegion <- aMatDateRegion[ purrr::map_lgl(aMatDateRegion, function(x) nrow(x) > 0 ) ]
dfPlotQuery <- purrr::map_df( aMatDateRegion, Matrix::summary, .id = "Type" )
head(dfPlotQuery)
Type i j x
1 city.driving 1 1 100.00
2 city.driving 2 1 100.73
3 city.driving 3 1 102.86
4 city.driving 4 1 102.65
5 city.driving 5 1 109.39
6 city.driving 6 1 109.62
ggplot2::ggplot(dfPlotQuery) +
ggplot2::geom_tile( ggplot2::aes( x = j, y = i, fill = log10(x)), color = "white") +
ggplot2::scale_fill_gradient(low = "white", high = "blue") +
ggplot2::xlab("Region") + ggplot2::ylab("Date") +
ggplot2::facet_wrap( ~Type, scales = "free", ncol = 2)

Here we take a “closer look” to one of the plots using a dedicated d3heatmap plot:
d3heatmap::d3heatmap( x = aMatDateRegion[["country/region.driving"]], Rowv = FALSE )
Nearest neighbors graphs
Graphs overview
Here we create nearest neighbor graphs of the contingency matrices computed above and plot cluster the nodes:
th <- 0.94
aNNGraphs <-
purrr::map( aMatDateRegion, function(m) {
m2 <- cor(as.matrix(m))
for( i in 1:nrow(m2) ) {
m2[i,i] <- 0
}
m2 <- as( m2, "dgCMatrix")
m2@x[ m2@x <= th ] <- 0
#m2@x[ m2@x > th ] <- 1
igraph::graph_from_adjacency_matrix(Matrix::drop0(m2), weighted = TRUE, mode = "undirected")
})
ind <- 3
ceb <- cluster_edge_betweenness(aNNGraphs[[ind]])
dendPlot(ceb, mode="hclust", main = names(aNNGraphs)[[ind]])
plot(ceb, aNNGraphs[[ind]], vertex.size=1, vertex.label=NA, main = names(aNNGraphs)[[ind]])
Time series analysis
Time series
In this section for each date we sum all cases over the region-transportation pairs, make a time series, and plot them.
Remark: In the plots the Sundays are indicated with orange dashed lines.
Here we make the time series:
aDateStringToDateObject <- unique( dfAppleMobilityLongForm[, c("Date", "DateObject")] )
aDateStringToDateObject <- setNames( aDateStringToDateObject$DateObject, aDateStringToDateObject$Date )
aDateStringToDateObject <- as.POSIXct(aDateStringToDateObject)
aTSDirReqByCountry <- purrr::map( aMatDateRegion, function(m) rowSums(m) )
matTS <- do.call( cbind, aTSDirReqByCountry)
zooObj <- zoo::zoo( x = matTS, as.POSIXct(rownames(matTS)) )
Here we plot them:
autoplot(zooObj) +
aes(colour = NULL, linetype = NULL) +
facet_grid(Series ~ ., scales = "free_y") +
geom_vline( xintercept = aDateStringToDateObject[weekdays(aDateStringToDateObject) == "Sunday"], color = "orange", linetype = "dashed", size = 0.3 )

Observation: In the time series plots the Sundays are indicated with orange dashed lines. We can see that from Monday to Thursday people are more familiar with their trips than say on Fridays and Saturdays. We can also see that on Sundays people (on average) are more familiar with their trips or simply travel less.
“Forecast”
He we do “forecast” for code-workflow demonstration purposes – the forecasts should not be taken seriously.
Fit a time series model to the time series:
aTSModels <- purrr::map( names(zooObj), function(x) { forecast::auto.arima( zoo( x = zooObj[,x], order.by = index(zooObj) ) ) } )
aTSModels <- purrr::map( names(zooObj), function(x) forecast::forecast( as.matrix(zooObj)[,x] ) )
names(aTSModels) <- names(zooObj)
Plot data and forecast:
lsPlots <- purrr::map( names(aTSModels), function(x) autoplot(aTSModels[[x]]) + ylab("Volume") + ggtitle(x) )
names(lsPlots) <- names(aTSModels)
do.call( gridExtra::grid.arrange, lsPlots )

---
title: "Apple mobility trends data visualization"
author: Anton Antonov
date: 2020-05-13
output: html_notebook
---


# Introduction

I this notebook we ingest and visualize the mobility trends data provided by Apple, [APPL1].

We take the following steps:

1. Download the data

2. Import the data and summarise it

3. Transform the data into long form

4. Partition the data into subsets that correspond to combinations of geographical regions and transportation types

5. Make contingency matrices and corresponding heat-map plots

6. Make nearest neighbors graphs over the contingency matrices and plot communities

7. Plot the corresponding time series

## Data description

### From Apple’s page [https://www.apple.com/covid19/mobility](https://www.apple.com/covid19/mobility)

**About This Data**
The CSV file and charts on this site show a relative volume of directions requests per country/region or city compared to a baseline volume on January 13th, 2020.
We define our day as midnight-to-midnight, Pacific time. Cities represent usage in greater metropolitan areas and are stably defined during this period. In many countries/regions and cities, relative volume has increased since January 13th, consistent with normal, seasonal usage of Apple Maps. Day of week effects are important to normalize as you use this data.
Data that is sent from users’ devices to the Maps service is associated with random, rotating identifiers so Apple doesn’t have a profile of your movements and searches. Apple Maps has no demographic information about our users, so we can’t make any statements about the representativeness of our usage against the overall population.

## Observations

The observations listed in this subsection are also placed under the relevant statistics in the following sections and indicated with “**Observation**”.

- The directions requests volumes reference date for normalization is 2020-01-13 : all the values in that column are $100$.

- From the community clusters of the nearest neighbor graphs (derived from the time series of the normalized driving directions requests volume) we see that countries and cities are clustered in expected ways. For example, in the community graph plot corresponding to “{city, driving}” the cities Oslo, Copenhagen, Helsinki, Stockholm, and Zurich are placed in the same cluster. In the graphs corresponding to “{city, transit}” and “{city, walking}” the Japanese cities Tokyo, Osaka, Nagoya, and Fukuoka are clustered together.

- In the time series plots the Sundays are indicated with orange dashed lines. We can see that from Monday to Thursday people are more familiar with their trips than say on Fridays and Saturdays. We can also see that on Sundays people (on average) are more familiar with their trips or simply travel less.

# Load packages

```{r}
library(Matrix)
library(tidyverse)
library(ggplot2)
library(gridExtra)
library(d3heatmap)
library(igraph)
library(zoo)
```


## Data ingestion

Apple mobile data was provided in this WWW page: [https://www.apple.com/covid19/mobility](https://www.apple.com/covid19/mobility) , [APPL1]. (The data has to be download from that web page -- there is an “agreement to terms”, etc.)

```{r}
dfAppleMobility <- read.csv( "~/Downloads/applemobilitytrends-2020-05-26.csv", stringsAsFactors = FALSE)
names(dfAppleMobility) <- gsub( "^X", "", names(dfAppleMobility))
names(dfAppleMobility) <- gsub( ".", "-", names(dfAppleMobility), fixed = TRUE)
```

```{r}
dfAppleMobility
```


**Observation:** The directions requests volumes reference date for normalization is 2020-01-13 : all the values in that column are $100$.

Data dimensions:

```{r}
dim(dfAppleMobility)
```

Data summary:

```{r}
summary(as.data.frame(unclass(dfAppleMobility[,1:3]), stringsAsFactors = TRUE))
```

Number of unique “country/region” values:

```{r}
dfAppleMobility %>% 
  dplyr::filter( geo_type == "country/region") %>% 
  dplyr::pull("region") %>%
  unique %>% 
  length
```

Number of unique “city” values:

```{r}
dfAppleMobility %>% 
  dplyr::filter( geo_type == "city") %>% 
  dplyr::pull("region") %>%
  unique %>% 
  length
```


All unique geo types:

```{r}
lsGeoTypes <- unique(dfAppleMobility[["geo_type"]])
lsGeoTypes
```

All unique transportation types:

```{r}
lsTransportationTypes <-  unique(dfAppleMobility[["transportation_type"]])
lsTransportationTypes
```

# Data transformation

It is better to have the data in [long form (narrow form)](https://en.wikipedia.org/wiki/Wide_and_narrow_data). 
For that I am using the package ["tidyr"](https://tidyr.tidyverse.org).

```{r}
# lsIDColumnNames <- c("geo_type", "region", "transportation_type") # For the initial dataset released by Apple.
lsIDColumnNames <- c("geo_type", "region", "transportation_type", "alternative_name", "sub-region", "country" )
dfAppleMobilityLongForm <- tidyr::pivot_longer( data = dfAppleMobility, cols = setdiff( names(dfAppleMobility), lsIDColumnNames), names_to = "Date", values_to = "Value" )
dim(dfAppleMobilityLongForm)
```

Remove the rows with “empty” values:

```{r}
dfAppleMobilityLongForm <- dfAppleMobilityLongForm[ complete.cases(dfAppleMobilityLongForm), ]
dim(dfAppleMobilityLongForm)
```

Add the "DateObject" column:

```{r}
dfAppleMobilityLongForm$DateObject <- as.POSIXct( dfAppleMobilityLongForm$Date, format = "%Y-%m-%d", origin = "1970-01-01" )
```

Add "day name" (“day of the week”) field:

```{r}
dfAppleMobilityLongForm$DayName <- weekdays(dfAppleMobilityLongForm$DateObject)
```

Here is sample of the transformed data:

```{r}
set.seed(3232)
dfAppleMobilityLongForm %>% dplyr::sample_n( 10 )
```

Here is summary:

```{r}
summary(as.data.frame(unclass(dfAppleMobilityLongForm), stringsAsFactors = TRUE))
```

Partition the data into geo types × transportation types:

```{r}
dfAppleMobilityLongForm %>% 
  dplyr::group_by( geo_type, transportation_type) %>% 
  dplyr::count()
```

```{r}
aQueries <- split(dfAppleMobilityLongForm,  dfAppleMobilityLongForm[,c("geo_type", "transportation_type")] )
```

# Heat-map plots

We can visualize the data using heat-map plots.

**Remark:** Using the contingency matrices prepared for the heat-map plots we can do further analysis, like, finding correlations or nearest neighbors. (See below.)

Cross-tabulate dates with regions:

```{r}
aMatDateRegion <- purrr::map( aQueries, function(dfX) { xtabs( formula = Value ~ Date + region, data = dfX, sparse = TRUE ) } )
aMatDateRegion <- aMatDateRegion[ purrr::map_lgl(aMatDateRegion, function(x) nrow(x) > 0 ) ]
```



```{r}
dfPlotQuery <- purrr::map_df( aMatDateRegion, Matrix::summary, .id = "Type" )
head(dfPlotQuery)
```

```{r, fig.width = 8, fig.hight = 8, warning=FALSE}
ggplot2::ggplot(dfPlotQuery) +
  ggplot2::geom_tile( ggplot2::aes( x = j, y = i, fill = log10(x)), color = "white") +
  ggplot2::scale_fill_gradient(low = "white", high = "blue") +
  ggplot2::xlab("Region") + ggplot2::ylab("Date") + 
  ggplot2::facet_wrap( ~Type, scales = "free", ncol = 2)
```

Here we take a "closer look" to one of the plots using a dedicated `d3heatmap` plot:

```{r}
d3heatmap::d3heatmap( x = aMatDateRegion[["country/region.driving"]], Rowv = FALSE )
```

# Nearest neighbors graphs

## Graphs overview

Here we create nearest neighbor graphs of the contingency matrices computed above and plot cluster the nodes:

```{r}
th <- 0.94
aNNGraphs <- 
  purrr::map( aMatDateRegion, function(m) { 
    m2 <- cor(as.matrix(m))
    for( i in 1:nrow(m2) ) {
      m2[i,i] <- 0
    }
    m2 <- as( m2, "dgCMatrix") 
    m2@x[ m2@x <= th ] <- 0
    #m2@x[ m2@x > th ] <- 1
    igraph::graph_from_adjacency_matrix(Matrix::drop0(m2), weighted = TRUE, mode = "undirected")
  })
```

```{r, eval=FALSE, warning=FALSE}
ind <- 3
ceb <- cluster_edge_betweenness(aNNGraphs[[ind]])  
dendPlot(ceb, mode="hclust", main = names(aNNGraphs)[[ind]])
```

```{r, eval=FALSE}
plot(ceb, aNNGraphs[[ind]], vertex.size=1, vertex.label=NA, main = names(aNNGraphs)[[ind]])
```

# Time series analysis

## Time series

In this section for each date we sum all cases over the region-transportation pairs, make a time series, and plot them. 

**Remark:** In the plots the Sundays are indicated with orange dashed lines.

Here we make the time series:

```{r}
aDateStringToDateObject <- unique( dfAppleMobilityLongForm[, c("Date", "DateObject")] )
aDateStringToDateObject <- setNames( aDateStringToDateObject$DateObject, aDateStringToDateObject$Date )
aDateStringToDateObject <- as.POSIXct(aDateStringToDateObject)
aTSDirReqByCountry <-  purrr::map( aMatDateRegion, function(m) rowSums(m) )
```

```{r}
matTS <- do.call( cbind, aTSDirReqByCountry)
```

```{r}
zooObj <- zoo::zoo( x = matTS, as.POSIXct(rownames(matTS)) )
```

Here we plot them:


```{r, fig.height=6, fig.width=6}
autoplot(zooObj) +
  aes(colour = NULL, linetype = NULL) +
	facet_grid(Series ~ ., scales = "free_y") +
  geom_vline( xintercept = aDateStringToDateObject[weekdays(aDateStringToDateObject) == "Sunday"], color = "orange", linetype = "dashed", size = 0.3 )
```


**Observation:** In the time series plots the Sundays are indicated with orange dashed lines. 
We can see that from Monday to Thursday people are more familiar with their trips than say on Fridays and Saturdays. 
We can also see that on Sundays people (on average) are more familiar with their trips or simply travel less.

## “Forecast”

He we do “forecast” for code-workflow demonstration purposes -- the forecasts should not be taken seriously.

Fit a time series model to the time series:

```{r}
aTSModels <- purrr::map( names(zooObj), function(x) { forecast::auto.arima( zoo( x = zooObj[,x], order.by = index(zooObj) ) ) } )
```

```{r}
aTSModels <- purrr::map( names(zooObj), function(x) forecast::forecast( as.matrix(zooObj)[,x] ) )
names(aTSModels) <- names(zooObj)
```

Plot data and forecast:

```{r}
lsPlots <- purrr::map( names(aTSModels), function(x) autoplot(aTSModels[[x]]) + ylab("Volume") + ggtitle(x) )
names(lsPlots) <- names(aTSModels)
```


```{r}
do.call( gridExtra::grid.arrange, lsPlots )
```

# References

[APPL1] Apple Inc., [Mobility Trends Reports](https://www.apple.com/covid19/mobility), (2020), [apple.com](https://www.apple.com).

[AA1] Anton Antonov, 
["Apple mobility trends data visualization"](https://github.com/antononcube/SystemModeling/blob/master/Projects/Coronavirus-propagation-dynamics/Documents/Apple-mobility-trends-data-visualization.md), 
(2020), 
[SystemModeling at GitHub](https://github.com/antononcube/SystemModeling).

[AA2] Anton Antonov, 
["NY Times COVID-19 data visualization"](https://github.com/antononcube/SystemModeling/blob/master/Projects/Coronavirus-propagation-dynamics/Documents/NYTimes-COVID-19-data-visualization.md), 
(2020), 
[SystemModeling at GitHub](https://github.com/antononcube/SystemModeling).

